Neutrophils Contribute to the Protection Conferred by ArtinM against Intracellular Pathogens: A Study on Leishmania major

ArtinM, a D-mannose binding lectin from Artocarpus heterophyllus, has immunomodulatory activities through its interaction with N-glycans of immune cells, culminating with the establishment of T helper type 1 (Th1) immunity. This interaction protects mice against intracellular pathogens, including Leishmania major and Leishmania amazonensis. ArtinM induces neutrophils activation, which is known to account for both resistance to pathogens and host tissue injury. Although exacerbated inflammation was not observed in ArtinM-treated animals, assessment of neutrophil responses to ArtinM is required to envisage its possible application to design a novel immunomodulatory agent based on carbohydrate recognition. Herein, we focus on the mechanisms through which neutrophils contribute to ArtinM-induced protection against Leishmania, without exacerbating inflammation. For this purpose, human neutrophils treated with ArtinM and infected with Leishmania major were analyzed together with untreated and uninfected controls, based on their ability to eliminate the parasite, release cytokines, degranulate, produce reactive oxygen species (ROS), form neutrophil extracellular traps (NETs) and change life span. We demonstrate that ArtinM-stimulated neutrophils enhanced L. major clearance and at least duplicated tumor necrosis factor (TNF) and interleukin-1beta (IL-1β) release; otherwise, transforming growth factor-beta (TGF-β) production was reduced by half. Furthermore, ROS production and cell degranulation were augmented. The life span of ArtinM-stimulated neutrophils decreased and they did not form NETs when infected with L. major. We postulate that the enhanced leishmanicidal ability of ArtinM-stimulated neutrophils is due to augmented release of inflammatory cytokines, ROS production, and cell degranulation, whereas host tissue integrity is favored by their shortened life span and the absence of NET formation. Our results reinforce the idea that ArtinM may be considered an appropriate molecular template for the construction of an efficient anti-infective agent.     

Phenotype Classification of Zebrafish Embryos by Supervised Learning

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.     

Can Newts Cope with the Heat? Disparate Thermoregulatory Strategies of Two Sympatric Species in Water

Many ectotherms effectively reduce their exposure to low or high environmental temperatures using behavioral thermoregulation. In terrestrial ectotherms, thermoregulatory strategies range from accurate thermoregulation to thermoconformity according to the costs and limits of thermoregulation, while in aquatic taxa the quantification of behavioral thermoregulation have received limited attention. We examined thermoregulation in two sympatric newt species, Ichthyosaura alpestris and Lissotriton vulgaris, exposed to elevated water temperatures under semi-natural conditions. According to a recent theory, we predicted that species for which elevated water temperatures pose a lower thermal quality habitat, would thermoregulate more effectively than species in thermally benign conditions. In the laboratory thermal gradient, L. vulgaris maintained higher body temperatures than I. alpestris. Semi-natural thermal conditions provided better thermal quality of habitat for L. vulgaris than for I. alpestris. Thermoregulatory indices indicated that I. alpestris actively thermoregulated its body temperature, whereas L. vulgaris remained passive to the thermal heterogeneity of aquatic environment. In the face of elevated water temperatures, sympatric newt species employed disparate thermoregulatory strategies according to the species-specific quality of the thermal habitat. Both strategies reduced newt exposure to suboptimal water temperatures with the same accuracy but with or without the costs of thermoregulation. The quantification of behavioral thermoregulation proves to be an important conceptual and methodological tool for thermal ecology studies not only in terrestrial but also in aquatic ectotherms.     

Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room

A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69–0.99) and a specificity 0.84 (95% CI 0.58–0.94) with an AUC of 0.93 (95% CI 0.89–1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85–1.00) and specificity of 0.95 (95% CI 0.74–0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.     

Structural Insight of a Trimodular Halophilic Cellulase with a Family 46 Carbohydrate-Binding Module

Cellulases are the key enzymes used in the biofuel industry. A typical cellulase contains a catalytic domain connected to a carbohydrate-binding module (CBM) through a flexible linker. Here we report the structure of an atypical trimodular cellulase which harbors a catalytic domain, a CBM46 domain and a rigid CBM_X domain between them. The catalytic domain shows the features of GH5 family, while the CBM46 domain has a sandwich-like structure. The catalytic domain and the CBM46 domain form an extended substrate binding cleft, within which several tryptophan residues are well exposed. Mutagenesis assays indicate that these residues are essential for the enzymatic activities. Gel affinity electrophoresis shows that these tryptophan residues are involved in the polysaccharide substrate binding. Also, electrostatic potential analysis indicates that almost the entire solvent accessible surface of CelB is negatively charged, which is consistent with the halophilic nature of this enzyme.     

Solving the N-Queens problem with a binary Hopfield-type network

The application of a discrete Hopfield-type neural network to solving the NP-Hard optimization problem — the N-Queens Problem (NQP) — is presented. The applied network is binary, and at every moment each neuron potential is equal to either 0 or 1. The network can be implemented in the asynchronous mode as well as in the synchronous one with n parallel running processors. In both cases the convergence rate is up to 100%, and the experimental estimate of the average computational complexity is polynomial. Based on the computer simulation results and the theoretical analysis, the proper network parameters are established. The behaviour of the network is explained.